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Mixed Effects Models are Sometimes Terrible ...
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Abstract:
Mixed-effects models have emerged as the gold standard of statistical analysis in different sub-fields of linguistics (Baayen, Davidson & Bates, 2008; Johnson, 2009; Barr, et al, 2013; Gries, 2015). One problematic feature of these models is their failure to converge under maximal (or even near-maximal) random effects structures. The lack of convergence is relatively unaddressed in linguistics and when it is addressed has resulted in statistical practices (e.g. Jaeger, 2009; Gries, 2015; Bates, et al, 2015b) that are premised on the idea that non-convergence is an indication that a random effects structure is over-specified (or not parsimonious), the parsimonious convergence hypothesis (PCH). We test the PCH by running simulations in lme4 under two sets of assumptions for both a linear dependent variable and a binary dependent variable in order to assess the rate of non-convergence for both types of mixed effects models when a known maximal effect structure is used to generate the data (i.e. when ... : Write up for poster presented at Linguistic Society of America 2017: Eager, Christopher and Joseph Roy. Mixed Effects are Sometimes Terrible. Linguistic Society of America, Poster (January 5-8, 2017) ...
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Keyword:
Applications stat.AP; Computation stat.CO; FOS Computer and information sciences
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URL: https://arxiv.org/abs/1701.04858
https://dx.doi.org/10.48550/arxiv.1701.04858
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Confronting Quasi-Separation in Logistic Mixed Effects for Linguistic Data: A Bayesian Approach ...
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